The lack of knowledge regarding the pathogenesis and host immune response during SARS-CoV-2 infection has limited the development of effective treatments. Thus, we longitudinally investigated the dynamic changes in peripheral blood lymphocyte subsets and parallel changes in cytokine levels in COVID-19 patients with different disease severities to further address disease pathogenesis.
A total of 67 patients (10 moderate, 38 severe and 19 critical cases) with COVID-19 admitted to a tertiary care hospital in Wuhan from February 8th to April 6th, 2020 were retrospectively studied. Dynamic data of lymphocyte subsets and inflammatory cytokines were collected.
On admission, compared with moderate cases, severe and critical cases showed significantly decreased levels of total lymphocytes, T lymphocytes, CD4T cells, CD8T cells, B cells and NK cells. IL-6 and IL-10 were significantly higher in the critical group. During the following hospitalization period, most of the lymphocyte subsets in the critical group began to recover to levels comparable to those in the severe group from the fourth week after illness onset, except for NK cells, which recovered after the sixth week. A sustained decrease in the lymphocyte subsets and an increase in IL-6 and IL-10 were observed in the nonsurvivors until death. There was a strong negative correlation between IL-6 and IL-10 and total lymphocytes, T lymphocytes, CD4T cells, CD8T cells and NK cells.
A sustained decrease in lymphocyte subsets, especially CD4T cells and NK cells, interacting with proinflammatory cytokine storms was associated with severe disease and poor prognosis in COVID-19.
A sustained decrease in lymphocyte subsets, especially CD4+ T cells and NK cells, interacting with proinflammatory cytokine storms was associated with severe disease and poor prognosis in COVID-19.Variants that regulate transcription, such as expression quantitative trait loci (eQTL), have shown enrichment in genome-wide association studies (GWAS) for mammalian complex traits. However, no study has reported eQTL in sheep, although it is an important agricultural species for which many GWAS of complex meat traits have been conducted. Using RNA sequence data produced from liver and muscle from 149 sheep and imputed whole-genome single nucleotide polymorphisms (SNPs), our aim was to dissect the genetic architecture of the transcriptome by associating sheep genotypes with three major molecular phenotypes including gene expression (geQTL), exon expression (eeQTL) and RNA splicing (sQTL). We also examined these three types of eQTL for their enrichment in GWAS of multi-meat traits and fatty acid profiles.
Whereas a relatively small number of molecular phenotypes were significantly heritable (h?&gt;?0, P?&lt;?0.05), their mean heritability ranged from 0.67 to 0.73 for liver and from 0.71 to 0.77 for muscprevalent. Many eQTL were also QTL for meat traits. https://www.selleckchem.com/products/cfse.html Our study fills a gap in the knowledge on the regulatory variants and their role in complex traits for the sheep model.
We detected a large number of significant eQTL and found that the overlap of variants between eQTL types and tissues was prevalent. Many eQTL were also QTL for meat traits. Our study fills a gap in the knowledge on the regulatory variants and their role in complex traits for the sheep model.Long non-coding RNAs (lncRNAs) regulate diverse biological processes via interactions with proteins. Since the experimental methods to identify these interactions are expensive and time-consuming, many computational methods have been proposed. Although these computational methods have achieved promising prediction performance, they neglect the fact that a gene may encode multiple protein isoforms and different isoforms of the same gene may interact differently with the same lncRNA.
In this study, we propose a novel method, DeepLPI, for predicting the interactions between lncRNAs and protein isoforms. Our method uses sequence and structure data to extract intrinsic features and expression data to extract topological features. To combine these different data, we adopt a hybrid framework by integrating a multimodal deep learning neural network and a conditional random field. To overcome the lack of known interactions between lncRNAs and protein isoforms, we apply a multiple instance learning (MIL) approach. . We believe that such an approach would find more applications in predicting other functional roles of RNAs and proteins.The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemented pipelines are commonly standalone software, optimized on a specific public data set. Therefore, this paper introduces the open-source Python library MIScnn.
The aim of MIScnn is to provide an intuitive API allowing fast building of medical image segmentation pipelines including data I/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art deep learning models and model utilization like training, prediction, as well as fully automatic evaluation (e.g. cross-validation). Similarly, high configurability and multiple open interfaces allow full pipeline customization.
Running a cross-validation with MIScnn on the Kidney Tumor Segmentation Challenge 2019 data set (multi-class semantic segmentation with 300 CT scans) resulted into a powerful predictor based on the standard 3D U-Net model.
With this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. The source code for MIScnn is available in the Git repository https//github.com/frankkramer-lab/MIScnn .
With this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. The source code for MIScnn is available in the Git repository https//github.com/frankkramer-lab/MIScnn .